The theory of compressed sensing shows that the original signal can be recovered by low sampling rate, so it is often used in the field of optical imaging. To solve the problem of excessive amount of image data and large computational burden, a new method based on column blocking and mixed blocking method is proposed in this paper. Simulation experiments and comparative analysis show that the proposed column blocking method has improved the quality of image reconstruction to a certain extent, while the mixed blocking method has significantly improved the speed and quality of image reconstruction.
With the development of the integrated circuit manufacturing process, the critical dimension of optical lithography is reduced. Due to the optical diffraction effect, the influence of the distortion of the lithography output pattern on the integrated circuit is gradually increasing. Mask optimization in lithography is a very critical issue. In this paper, a residual network-based mask optimization method is proposed. Using the optimization masks generated by the traditional gradient descent method and its corresponding initial input masks as the training set, the residual network is trained by the inverse lithography optimization process. The parameters of the residual network weight layer are optimized. The optimized results are projected in the forward lithography model to obtain an exposure pattern of the wafer. Compared with the traditional gradient descent method, this method can improve the calculation efficiency and realize the distortion correction of the images generated by lithography.
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